Random Forest-Based Classification of Greywater Filtration Media for Intelligent Biofiltration Systems

Abstrak

The increasing volume of domestic wastewater, particularly greywater, has raised the demand for intelligent and adaptive treatment systems to support efficient water reuse. This study aims to develop a classification model for filtration media types (physical, chemical, and biological) based on water quality data using the Random Forest algorithm. Initial labeling was conducted using the K-Means Clustering method on a publicly available dataset simulated as greywater, based on ten key water quality parameters relevant to irrigation and environmental standards. Model evaluation demonstrated excellent classification performance, with a macro F1-score reaching 0.97 and consistent results in both 5-fold and 10-fold cross-validation. These findings indicate that the proposed model can be integrated into an IoT-based biofiltration system as an automated classification logic to support adaptive, efficient, and reusable household wastewater treatment in the context of irrigation.